Recall that all models are trained for seven glimpses. In Figure REF , we assess their inference-time generalizability for a large number of glimpses by testing the models for T=49. Note that our method would have observed an entire image by then. We notice that, during the inference-time, RAM does not generalize well to a sequence of glimpses that are longer than the training time; [1]}, [2]}, [3]} also make a similar observation. Our method and RAM+ show greater generalizability than RAM. While the Random baseline is the most generalizable, our method outperforms it with an optimal number of glimpses. The accuracy achieved by our model with an optimal number of glimpses is lower than the CNN; however, the CNN is trained exclusively on complete images, and our model is not. Note that the primary motivation for the attention mechanism is to achieve higher accuracy using limited time and constrained resources. If time and resources are available, one can instead collect a large number of random glimpses and use a non-attentive CNN.
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